ScopeMap: An AI‐Assisted, Human‐in‐the‐Loop Workflow for Mapping Reaction Scope and Boundaries

Abstract

ABSTRACT Assessing the generality of synthetic methods is a cornerstone of organic chemistry, yet traditional manual selection and current optimization‐driven algorithms often fail to delineate reaction boundaries, clustering instead in high‐reactivity regions. Herein, we introduce ScopeMap, an iterative, human‐in‐the‐loop workflow designed to efficiently map functional limits rather than merely maximizing performance. Leveraging a modified Centroidal Voronoi Tessellation (CVT) algorithm with a dynamic geometric repulsion potential, ScopeMap transforms negative experimental feedback into geometric constraints, actively steering sampling toward unexplored frontiers. Validated against a comprehensive dataset of biomimetic aldol reactions and a cobalt‐catalyzed coupling system, the workflow achieves greater substrate diversity with a smaller selection of examples. By utilizing a representative subset comprising fewer than 4% of the substrate space, it successfully predicts the reactivity of the reaction space with an F1 score exceeding 90%. Furthermore, we establish the U‐Score and R‐Score—metrics derived from spatial entropy and mean squared distance (MSD)—to provide a standardized framework for quantifying sampling uniformity and representativeness. This work offers a resource‐efficient paradigm for defining reaction generality, shifting the focus from exhaustive data enumeration to information‐dense boundary mapping.